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Data Labeling: Jumpstarting Artificial Intelligence and Machine Learning Applications


Modern Artificial Intelligence (AI) products may seem like science fiction, but for today’s organizations, it’s a key integrated component that contributes to the growth for a business and acts as an ideal way to solve seemingly insurmountable issues in the digital age. The idea, at its core, is relatively simple since data is the new oil that powers a myriad of the transformative technology we see today including AI, advanced predictive analytics, robotics, Machine Learning (ML), Internet of Things (IoT), and many more. The effectiveness of data is inevitably the driver of growth and change, creating new business infrastructure, innovations, and crucially new economics. Data Labeling is a key component to success.

Data Labeling to Drive the AI and ML Explosion

Nevertheless, in some organizations, data is left untapped and businesses are sitting on a huge heap of uncategorized data. As such, data labeling is the gateway for addressing this key data challenge. The labeled data is a catalyzer to train machine learning systems and AI models in critical areas such as image recognition and speech recognition. Generally, data labeling gives AI its power and general purpose, by directly acting upon data that is relevant to decision-making and determining future outcomes. While data labeling is seemingly a cakewalk required for training a myriad of AI models (models that require data to simulate human thinking processes) and ML models (a vehicle that drives AI development, giving access to data and letting them learn for themselves, without the need for any explicit programming); it takes a lot of time to glean, define, and label data, and implement and control data streams to get them prepared for achieving positive and reliable results.

Most often, AI and ML models are trained via supervised learning, where the robust models are fed with a huge amount of labeled data, which has previously been manually categorized by humans. This type of learning is commonly seen in ML algorithms for classification (organizing labeled datasets) and regression (prediction of trends from the labeled data to determine future events). Currently, ML with deep neural networks requires colossal datasets that need to be labeled via massive human intervention, attention, and effort. This painstaking task will eventually slow down the innovation process and the rate of productivity growth.

The next waves of AI and deep learning models are fueled by unsupervised learning, which reduces the need for labeled datasets and leverages raw, unlabeled data to train the practical applications of AI. In this way of unsupervised learning techniques, the system can perform more complex processing tasks than supervised learning systems, since it finds the structure on its own, for example—learning to sort females from male. Unsupervised learning has more complex algorithms than supervised learning since it has very limited information about the data. This type of learning can be used in applications such as grouping and clustering, density estimation and dimensionality reduction.

Take the case of examining a batch of height and weight data for a specific age group of males and females. In supervised learning, we know what kind of data we are dealing with; while, in unsupervised learning any given sample from the data will not contain any additional information stating the height and weight data of a male or female. Here we use a clustering algorithm, which groups a set of objects based on their physical attributes—such as segregating a set of males and females based on their height and weight. Self-Organizing Maps (SOMs), a neural network method which is widely used in gene clustering, plays a vital role to ensure the clustered objects in the same group are more similar to each other comparatively to those in other groups. These SOMs assume a topological structure among the cluster units and effectively map additional information to the input data. We can feed this data back into the supervised learning algorithm as training data and use the model to make predictions on new hidden data.

Generally, which type of learning has driven most of the recent progress in the field? In unsupervised learning each piece of data passes through a model and there is no corresponding label that is paired with the same. Since data is unlabeled, this type of model will not be able to evaluate itself and understand how well it’s performing, and there is no way to measure its accuracy. On the other hand, with a fully supervised approach, the model will be able to predict the output and improve its own efficiency, becoming more accurate over time. The biggest drawback with this type of learning is that it requires a lot of computational time for training. In the case of voluminous and growing data, it may include anomalies and edge-cases to accurately predefine the rules and teach the algorithm to handle each unique situation. This can be a real challenge.  With supervised and unsupervised learning approaches holding their own pros and cons, it is safe to say, choosing either a supervised or unsupervised learning algorithm depends on the major factors related to the structure and volume of the data. However, supervised machine learning is the more common method that has application in a wide variety of industries; it’s wise to leverage both these types of learning approaches for building predictive data models, which in turn will be beneficial for stakeholders to come up with the best decisions across a variety of business challenges.

Seize New Opportunities

Recent studies reveal that more than 75 percent of organizations are investing in meaningful data since AI- and ML-powered smart machines and applications are set to dramatically increase over the next five years. With the recent AI boom, depending on data labeling and synthesis of a huge amount of training data to develop more predictive models, businesses should ensure that the data they collect also becomes more AI and ML-friendly.

Per the McKinsey analysis, modern companies foresee the fourth industrial revolution to increase revenues to $3.7 trillion by 2025 with AI being the cornerstone enabling this huge growth. Anticipating the size of this growth, Ness has pioneered to provide data labeling services for several of our clients. Currently, we are manually data labeling travel videos for one of our automotive clients, including labeling of entities in the road like cars, bikes, trucks, pedestrians, lanes, sign-boards, and many more.  Our labelers are extremely focused because even a single error or inaccuracy negatively affects a dataset’s quality and the overall performance of a predictive model. At Ness, we ensure efficiency and accuracy in every piece of data that we label. Our engineers are working to establish an automated data labeling system to further streamline the data labeling process.

With true innovation at its core, Ness is prepared to drive the rapid take-up of AI and ML applications across a range of sectors. We are aiming to bring positive changes to automotive safety with the goal of stimulating an overall upsurge in car safety for everyone on the road. Since most road accidents occur due to human error, we are focusing on enabling better situational awareness and control to make driving easier and safer through advanced driver-assistance systems. We believe that the progress of these safety approaches will be one of the main trends — by leveraging our technology and expertise, we can help drive future innovation.